Adaptive object in-path detection model for automated or semi-automated vehicle operation

ABSTRACT

A method for operating an advanced driver assistance systems (ADAS) includes determining a distance between an object and a vehicle, selecting a path estimation methodology from multiple path estimation methodologies based at least in part on a distance between the vehicle and the object, and activating at least one ADAS action in response to determining that the object intersects the estimated path.

TECHNICAL FIELD

The present disclosure relates generally to computational models fordetermining if a detected object is in a vehicle path, and morespecifically to a computational model configured to adapt thedetermination between at least two methods for estimating a vehiclepath.

BACKGROUND

Semi-automated vehicles utilize sensor arrays including video cameras,speed sensors, road sensors and the like to assist a driver in controlthe operation of the vehicle. Included within this control is thepathing (determining the travel route) of the vehicle, the detection ofobjects within the path, and determining an appropriate response to thedetection of one or more objects that are intersected by the estimatedpath. The decision process can be further complicated in semi-automatedvehicles when the operator alters the path unexpectedly and/or theobject may move unexpectedly.

Driver assistance semi-automated vehicle systems are referred to asadvanced driver assistance systems (ADAS) and assist the driver inoperating the vehicle. The object detection strategy for ADAS requiressufficiently accurate in-path detection in order to prevent collisionsbetween the vehicle and any detected objects. Some existing ADAS areintegrated with one or more vehicle sensors, and the data from thevehicle sensors is utilized to determine the expected path of thevehicle.

SUMMARY OF THE INVENTION

According to one example, a method for operating an advanced driverassistance systems (ADAS) includes determining a distance between anobject and a vehicle, selecting a path estimation methodology from aplurality of path estimation methodologies based at least in part on adistance between the vehicle and the object, and activating at least oneadvanced driver assistance systems ADAS action in response todetermining that the object intersects the estimated path.

In another example of the above method, selecting the path estimationmethodology from the plurality of path estimation methodologiescomprises comparing the determined distance to a distance threshold andselecting a first path estimation methodology in response to thedetermined distance being less than or equal to the distance threshold.

In another example of any of the above methods, the first pathestimation methodology is a yaw rate only prediction methodology.

In another example of any of the above methods, selecting the pathestimation methodology from the plurality of path estimationmethodologies comprises comparing the determined distance to a distancethreshold and selecting a second path estimation methodology in responseto the determined distance being greater than the distance threshold.

In another example of any of the above methods, the second pathestimation methodology estimates a vehicle path based on a combinationof vehicle yaw rate and at least one of an additional sensor output anda vehicle location.

In another example of any of the above methods, the at least one of theadditional sensor output and the vehicle location is the additionalsensor output, and the additional sensor output comprises at least oneof an output of a lane detection system, camera, radar/LIDAR, and GPS.

In another example of any of the above methods, the at least one of theadditional sensor output and the vehicle location includes a geographicvehicle location.

In another example of any of the above methods, selecting the pathestimation methodology from the plurality of path estimationmethodologies based at least in part on the distance between the vehicleand the object includes selecting the path estimation methodology basedat least in part on the distance between the vehicle and the object andon a rate of travel of at least one of the vehicle and the object.

In another example of any of the above methods, selecting the pathestimation methodology from the plurality of path estimationmethodologies comprises comparing the determined distance to a distancethreshold with the distance threshold being a single threshold.

In another example of any of the above methods, selecting the pathestimation methodology from the plurality of path estimationmethodologies comprises comparing the determined distance to a distancethreshold with the distance threshold being at least partially dependentupon a rate of speed of the vehicle.

In another example of any of the above methods, selecting the pathestimation methodology from the plurality of path estimationmethodologies comprises comparing the determined distance to a distancethreshold with the distance threshold being at least partially dependentupon a rate of speed of the vehicle, as well as potentially theangle/rate of steering.

An exemplary vehicle controller includes a memory, a processor, and atleast one input configured to receive sensor outputs from a plurality ofsensors including a vehicle yaw sensor, at least one speed sensor and aplurality of cameras, and the memory stores an advanced driverassistance systems (ADAS) including an object detection system and apath estimation system, the ADAS being configured to select a pathestimation methodology form a plurality of path estimation methodologiesin response to the object detection system detecting an object, whereinthe detection is based at least in part on a distance between thevehicle and the detected object.

In another example of the above vehicle controller, selecting the pathestimation methodology from the plurality of path estimationmethodologies comprises comparing the distance between the vehicle andthe detected object to a distance threshold and selecting a first pathestimation methodology in response to the determined distance being lessthan or equal to the distance threshold.

In another example of any of the above vehicle controllers, the firstpath estimation methodology is a yaw rate only prediction methodology.

In another example of any of the above vehicle controllers, selectingthe path estimation methodology from the plurality of path estimationmethodologies comprises comparing the distance between the vehicle andthe detected object to a distance threshold and selecting a second pathestimation methodology in response to the determined distance beinggreater than the distance threshold.

In another example of any of the above vehicle controllers, the secondpath estimation methodology estimates a vehicle path based on acombination of vehicle yaw rate and at least one of an additional sensoroutput and a vehicle location.

In another example of any of the above vehicle controllers, the ADASfurther comprises an automated response system configured to enforce acorrective action in response to the detected object intersecting withthe estimated path and the detected distance being less than athreshold.

In another example of any of the above vehicle controllers, the ADASfurther comprises an automated response system configured to recommend acorrective action in response to the detected object intersecting withthe estimated path and the detected distance being greater than athreshold.

An exemplary non-transitory computer readable medium stores instructionsfor causing a vehicle controller to determine a distance between anobject and a vehicle, select a path estimation methodology from aplurality of path estimation methodologies based at least in part on adistance between the vehicle and the object, and activate at least oneADAS action in response to determining that the object intersects theestimated path.

In another example of the above computer readable medium, selecting thepath estimation methodology from the plurality of path estimationmethodologies comprises comparing the determined distance to a distancethreshold and selecting a first path estimation methodology in responseto the determined distance being less than or equal to the distancethreshold.

These and other features of the present invention can be best understoodfrom the following specification and drawings, the following of which isa brief description.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary vehicle including an object detectionsystem.

FIG. 2 illustrates an exemplary hybrid method for detecting when atarget (detected object) is in a vehicle path.

FIG. 3 illustrates an exemplary vehicle controller for implementing themethod of FIG. 2.

DETAILED DESCRIPTION

FIG. 1 schematically illustrates an exemplary vehicle 10 including anadvanced driver assistance system (ADAS) 20. In some examples, the ADASis a dedicated controller including a memory and a processor, with thememory storing instructions for operating the ADAS. In alternativeexamples, the ADAS is either a hardware or software component within alarge engine controller, and the instructions for operating the ADAS arestored within the larger engine controller. The ADAS 20 is integratedwith multiple sensors 30 disposed about the vehicle 10, and uses thesensor data to assist in operation of the vehicle 10. By way of examplethe sensors 30 can include lane detection systems, cameras,accelerometers, yaw sensors, and any similar type of sensor 30. Theexemplary ADAS 20 includes one or more internal systems configured toidentify the presence of objects, such as a tree 40 or approachingvehicle 50 in front of the vehicle 10. The object detection can beperformed using any object detection methodology capable of detecting anobject using the available sensors.

In addition to detecting the presence of an object in front of thevehicle 10, the ADAS 20 is tasked with determining whether an estimatedpath 60, 62 of the vehicle 10 will result in an intersection with theobject 40, 50 or not. Based on the determination, the ADAS 20 canrecommend or enforce corrective measures to avoid potential collisionwith the detected object 40, 50. The recommendation or enforcement ofcorrective measures is collectively referred to as an ADAS 20 action. Byway of example, if the detected object 40, 50 is a stationary object,such as the tree 40, and the ADAS 20 determines that the current path60, 62 will intersect with the tree 40, and collision is imminent, theADAS 20 can either prompt the driver to turn (recommend a correctivemeasure) or apply the vehicles brakes (enforce a corrective measure)depending on the distance and speed difference between the vehicle 10and the detected object 40, 50.

In order to improve the accuracy of the ADAS 20, the ADAS 20 includesmethodologies for multiple distinct ways of estimating or predicting thepath 60, 62 of the vehicle 10. Included in the multiple ways ofestimating the path 60, 62 are methodologies that utilize only theyaw-rate of the vehicle 10, and methodologies that fuse data from one ormore inputs from sensors 30 with a yaw-rate to determine the estimatedpath 60, 62. It is appreciated that other known or conventional pathestimation methodologies can be utilized in place of, or alongside, thepath estimation methodologies described herein. In such an example theADAS 20 incorporates further decision matrices to determine which pathestimation methodology to use, factoring in the particular strengths andweaknesses of each methodology.

Each methodology for determining the estimated path 60, 62 hascorresponding strengths and weaknesses. By way of example only, it isappreciated that some yaw-rate only path predictions provide for afaster ADAS 20 response but are less reliable and less stable at mid tolong range distances between the vehicle 10 and the object 40, 50 due tosensor accuracy and limits on yaw rate. Similarly, by way of exampleonly, some sensor fused path predictions are more reliable at longrange, but provide a slower response to any given driver action due tothe consideration of the fused data. As used herein, a sensor fusedmethodology is any path prediction scheme that estimates a vehicle path60, 62 based on distinct sensor inputs. By way of example, a lanedetection sensor output can be fused with the yaw-rate to estimate atravel path 60, 62 of the vehicle 10. In other examples the sensor datafused to the yaw-rate data can include radar/LIDAR detection indicatingthe barrier of the road. In another example, the fused data can includea combination of the yaw rate and available map data indicating adirection of the road 70 on which the vehicle 10 is traveling, availableGPS or other positioning system data indicative of a geographicpositioning of the vehicle 10 (collectively referred to as a geographicvehicle location), or step by step direction data of the vehicle 10.

With continued reference to FIG. 1, FIG. 2 schematically illustrates anexemplary method 100 by which the ADAS 20 determines which pathprediction system, and thus which estimated path 60, 62, to utilize indetecting if a collision is imminent. Initially, the ADAS 20, or anothervehicle system, detects an object 40, 50 forward of the vehicle in a“Detect Object” step 110. As used herein, “forward of the vehicle”refers to an object in the vehicles general direction of travel, and caninclude an object in the rear of the vehicle 10 when the vehicle 10 istraveling in reverse.

Once the object 40, 50 is detected, the ADAS 20 estimates a distancebetween the vehicle 10 and the object 40, 50, as well as a rate ofapproach between the vehicle 10 and the object 40, 50 in an “EstimateDistance Between Vehicle and Object” step 120. In alternative examples,the ADAS 20 only estimates the distance, and the rate of approach of thevehicle 10 is not considered in the determination process. In systemsincorporating the rate of approach, the rate of approach can beapproximated using a detected travel speed of the vehicle 10 orestimated based on a combination of the travel speed of the vehicle 10and additional factors.

Once the distance has been determined, the determined distance iscompared to a threshold distance in a “Compare Distance to Threshold”step 130. The threshold distance is, in one example, a predetermineddistance at which the available response time allows the sensor fusedpath determination sufficient time to respond and the ADAS 20 to react.In such examples, the specific predetermined distance can be anempirically determined threshold set via laboratory testing and vehicleoperation data from open world testing, and is stored within the vehicle10 ADAS 20 in a look up table.

In alternative examples, such as those where the estimation step 120also determines a rate of speed of the vehicle 10, the thresholddistance can be adjusted further or closer depending on the rate ofspeed at which the vehicle 10 is traveling. This adjustment allows theADAS 20 to compensate for a distance covered during an operatorsreaction time, as well as the ability of the vehicle 10 to respond to agiven object detection scheme. In the alternative examples, the lookuptable is more complicated and includes multiple thresholds with eachthreshold being dependent on other factors such as the travel speed ofthe vehicle 10.

In yet further examples, the particular threshold, or thresholds,utilized by the ADAS 20 to determine which path estimation methodologyto utilize can be determined via a machine learning system trained usingan empirical data set. In such an example the empirical data setincludes actual vehicle operation data and can include open worldtesting, laboratory testing, and/or a combination of the two.

When the comparison to the threshold determines that the distancebetween the vehicle 10 and the object 40, 50 is less than or equal tothe threshold, the ADAS 20 determines that the reliability of theyaw-only path estimation is sufficient and the decreased response timeis needed, and the ADAS 20 uses the yaw-only estimated path 62 todetermine an appropriate driver assistance response in a “User Yaw-OnlyEstimation” step 140. The driver assistance response in such a case canbe warning indicators, automatic breaking, automatic swerving, or othercollision avoidance techniques.

When the comparison to the threshold determines that the distancebetween the vehicle 10 and the object 40, 50 is greater than thethreshold, the ADAS 20 determines that the decreased response time isunnecessary and the increased mid to long range reliability is desirablein a “Use Sensor Fused Estimation” step 150. The driver assistanceresponse in such a case can include warning indicators, automaticbraking, automatic swerving, or any similar corrective action.

While the exemplary ADAS 20 described herein includes two methodologiesfor determining the estimated path 60, 62, it is appreciated that theADAS 20 can be expanded to include additional methodologies by one ofskill in the art. In such an example, threshold windows are utilized todetermine which of the multiple path estimation schemes should beutilized in any given situation.

In some examples, such as the example illustrated in FIG. 3 the ADAS 20can be a software module 210 stored within a memory 220 of a controller200 such as a vehicle controller. In such examples, the software module210 is configured to cause a processor 230 in the controller 200 toperform the method described at FIG. 2.

It is further understood that any of the above described concepts can beused alone or in combination with any or all of the other abovedescribed concepts. Although an embodiment of this invention has beendisclosed, a worker of ordinary skill in this art would recognize thatcertain modifications would come within the scope of this invention. Forthat reason, the following claims should be studied to determine thetrue scope and content of this invention.

1. A method for operating an advanced driver assistance systems (ADAS)comprising: determining a distance between an object and a vehicle;selecting a path estimation methodology from a plurality of pathestimation methodologies based at least in part on a distance betweenthe vehicle and the object; and activating at least one advanced driverassistance systems ADAS action in response to determining that theobject intersects the estimated path.
 2. The method of claim 1, whereinselecting the path estimation methodology from the plurality of pathestimation methodologies comprises comparing the determined distance toa distance threshold and selecting a first path estimation methodologyin response to the determined distance being less than or equal to thedistance threshold.
 3. The method of claim 2, wherein the first pathestimation methodology is a yaw rate only prediction methodology.
 4. Themethod of claim 1, wherein selecting the path estimation methodologyfrom the plurality of path estimation methodologies comprises comparingthe determined distance to a distance threshold and selecting a secondpath estimation methodology in response to the determined distance beinggreater than the distance threshold.
 5. The method of claim 4, whereinthe second path estimation methodology estimates a vehicle path based ona combination of vehicle yaw rate and at least one of an additionalsensor output and a vehicle location.
 6. The method of claim 5, whereinthe at least one of the additional sensor output and the vehiclelocation is the additional sensor output, and the additional sensoroutput comprises at least one of an output of a lane detection system,camera, radar/LIDAR, and GPS.
 7. The method of claim 5, wherein the atleast one of the additional sensor output and the vehicle locationincludes a geographic vehicle location.
 8. The method of claim 1,wherein selecting the path estimation methodology from the plurality ofpath estimation methodologies based at least in part on the distancebetween the vehicle and the object includes selecting the pathestimation methodology based at least in part on the distance betweenthe vehicle and the object and on a rate of travel of at least one ofthe vehicle and the object.
 9. The method of claim 1, wherein selectingthe path estimation methodology from the plurality of path estimationmethodologies comprises comparing the determined distance to a distancethreshold with the distance threshold being a single threshold.
 10. Themethod of claim 1, wherein selecting the path estimation methodologyfrom the plurality of path estimation methodologies comprises comparingthe determined distance to a distance threshold with the distancethreshold being at least partially dependent upon a rate of speed of thevehicle.
 11. The method of claim 10, wherein selecting the pathestimation methodology from the plurality of path estimationmethodologies comprises comparing the determined distance to a distancethreshold with the distance threshold being at least partially dependentupon a rate of speed of the vehicle, as well as potentially theangle/rate of steering.
 12. A vehicle controller comprising: a memory, aprocessor, and at least one input configured to receive sensor outputsfrom a plurality of sensors including a vehicle yaw sensor, at least onespeed sensor and a plurality of cameras; and the memory storing anadvanced driver assistance systems (ADAS) including an object detectionsystem and a path estimation system, the ADAS being configured to selecta path estimation methodology form a plurality of path estimationmethodologies in response to the object detection system detecting anobject, wherein the detection is based at least in part on a distancebetween the vehicle and the detected object.
 13. The vehicle controllerof claim 12, wherein selecting the path estimation methodology from theplurality of path estimation methodologies comprises comparing thedistance between the vehicle and the detected object to a distancethreshold and selecting a first path estimation methodology in responseto the determined distance being less than or equal to the distancethreshold.
 14. The vehicle controller of claim 13, wherein the firstpath estimation methodology is a yaw rate only prediction methodology.15. The vehicle controller of claim 12, wherein selecting the pathestimation methodology from the plurality of path estimationmethodologies comprises comparing the distance between the vehicle andthe detected object to a distance threshold and selecting a second pathestimation methodology in response to the determined distance beinggreater than the distance threshold.
 16. The vehicle controller of claim15, wherein the second path estimation methodology estimates a vehiclepath based on a combination of vehicle yaw rate and at least one of anadditional sensor output and a vehicle location.
 17. The vehiclecontroller of claim 12, wherein the ADAS further comprises an automatedresponse system configured to enforce a corrective action in response tothe detected object intersecting with the estimated path and thedetected distance being less than a threshold.
 18. The vehiclecontroller of claim 12, wherein the ADAS further comprises an automatedresponse system configured to recommend a corrective action in responseto the detected object intersecting with the estimated path and thedetected distance being greater than a threshold.
 19. A non-transitorycomputer readable medium storing instructions for causing a vehiclecontroller to: determine a distance between an object and a vehicle;select a path estimation methodology from a plurality of path estimationmethodologies based at least in part on a distance between the vehicleand the object; and activate at least one ADAS action in response todetermining that the object intersects the estimated path.
 20. Thenon-transitory computer readable medium of claim 20, wherein selectingthe path estimation methodology from the plurality of path estimationmethodologies comprises comparing the determined distance to a distancethreshold and selecting a first path estimation methodology in responseto the determined distance being less than or equal to the distancethreshold.